Assistant Professor / Associate Professor (Tenure Track)

National University of Singapore; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading educational institution in Singapore seeks a dedicated faculty member specializing in Artificial Intelligence within the medical field. Responsibilities include leading innovative AI research, collaborating across disciplines, and mentoring PhD students. A PhD in a quantitative discipline and a strong publication record are required. The position offers competitive salary, start-up funding, and access to advanced resources in a vibrant research environment.

Benefits

Start-up funding

Access to secure clinical data

Modern GPU and computing resources

Relocation support

Qualifications

• Strong evidence of AI application in biomedical contexts.
• Proven publication record in reputable journals.
• Solid grant activity as principal investigator or co-investigator.
• Prior teaching and supervision experience at graduate level is preferred.

Responsibilities

• Lead an innovative AI/ML research programme.
• Engage in collaborative research with clinicians and biomedical researchers.
• Mentor and supervise PhD students.
• Contribute to grant acquisition and high-impact publications.
• Support teaching in Biostatistics and Health Data Science.

Skills

Expertise in Artificial Intelligence

Research in biomedical sciences

Collaborative research capabilities

Problem-solving skills

Communication across disciplines

Education

PhD in Statistics, Biostatistics, Computer Science

Tools

High-performance computing resources

Job description

Job Description

Duke-NUS invites applications for a Tenure Track faculty position at the rank of Assistant or Associate Professor. We seek an outstanding Artificial Intelligence (AI) expert specializing in the medical or biomedical sciences, with demonstrated excellence in both methodological research and clinical application of artificial intelligence and machine learning (ML). The candidate will contribute to high-impact research addressing complex challenges where AI/ML is applied to key research areas, including population health, healthy longevity and aging, mental health and resilience, immunology, precision medicine, clinical decision-making, drug discovery, climate change, and biomedical discovery. This successful candidate will be based at the Centre for Biomedical Data Science (CBDS), which will come into effect from January 1, 2026, as a strategic merger of two mature Duke-NUS centres, namely, the Centre for Quantitative Medicine (CQM) and the Centre for Computational Biology (CCB). The incoming faculty member will also be an important player in the Duke-NUS AI + Medical Sciences Initiative (DAISI).

Key Responsibilities

• Lead an innovative research programme in AI/ML, with applications in medicine, clinical research, omics, imaging, or population health.
• Translate and implement advanced analytic approaches (e.g., multimodal AI) into clinical/biomedical settings with SingHealth/NUS collaborators.
• Engage in collaborative research with clinicians, biomedical researchers, and statisticians across SingHealth, NUS and Duke University.
• Mentor and supervise PhD students, promoting local capacity and excellence in methodology and translational AI.
• Actively contribute to grant acquisition, research leadership, and dissemination of results through high-impact publications.
• Support teaching in Biostatistics, Health Data Science, and related programs.

Job Requirements

Qualifications

• PhD in Statistics, Biostatistics, Computer Science, or a related quantitative discipline, with strong evidence of applying AI in biomedical/clinical contexts.
• Proven publication record in reputable journals and recognition for methodological and/or applied contributions in healthcare AI.
• Solid record of research grant activity, both as principal investigator and co-investigator.
• Excellent problem-solving skills and ability to communicate effectively across disciplines.
• Demonstrated experience with multidisciplinary projects, particularly at the intersection of method development and clinical/population health application.
• Prior teaching and supervision experience at the graduate level is preferred.

What we offer

• Start-up funding, commensurate with rank and the proposed research programme
• Access to secure clinical and multi-omics data environments
• Modern GPU and high-performance computing resources, plus dedicated research-engineering support
• Close integration with clinicians and clinical trial/implementation units
• Support for commercialization, intellectual property and industry partnerships
• Competitive salary and benefits, with relocation support where applicable.

How to apply

Submit your CV, a brief 2-page research statement, and up to 3 key publications. Application review continues until filled.

Contact

[e-mail]

Apply now

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